The NVIDIA Llama 3.1 8B Instruct FP4 model is the quantized language model of the Meta's Llama 3.1 8B model, which is an auto-regressive language model. For more information, please check here.
This model is ready for commercial and non-commercial use.
Third-Party Community Consideration
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Llama-3.1-8B-Instruct) Model Card.
License/Terms of Use:
GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License.
ADDITIONAL INFORMATION: Llama3 Community License Agreement. Built with Llama.
Deployment Geography:
Global, except in European Union
Use Case:
Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
**This model was developed based on Llama3.1-8B-Instruct
** Number of model parameters 8.0*10^9
Input:
Input Type(s): Text Input Format(s): String Input Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Input: Context length up to 128,000
Output:
Output Type(s): Text Output Format: String Output Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Output: N/A
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
The model is quantized with nvidia-modelopt v0.35.0
Post Training Quantization
This model was obtained by quantizing the weights and activations of Llama 3.1 8B Instruct to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks are quantized.
** Data Collection Method by Dataset: Undisclosed
** Labeling Method by Dataset: Undisclosed
** Properties: Undisclosed
Testing Dataset:
** Data Collection Method by Dataset: Undisclosed
** Labeling Method by Dataset: Undisclosed
** Properties: Undisclosed
Inference:
Engine: TensorRT-LLM Test Hardware: B200
Usage
Deploy with TensorRT-LLM
To serve the quantized checkpoint with TensorRT-LLM, follow the sample commands below with the TensorRT-LLM GitHub repo:
LLM API sample usage:
import asyncio
from tensorrt_llm import LLM, SamplingParams
def main():
llm = LLM(model="nvidia/Llama-3.1-8B-Instruct-FP4", attn_backend="FLASHINFER", backend="pytorch", tensor_parallel_size=1)
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
async def task(prompt: str):
output = await llm.generate_async(prompt, sampling_params)
print(
f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
)
async def main():
tasks = [task(prompt) for prompt in prompts]
await asyncio.gather(*tasks)
asyncio.run(main())
# The entry point of the program need to be protected for spawning processes.
if __name__ == '__main__':
main()
Ethical Considerations :
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should perform safety testing and tuning tailored to their specific applications of the model. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
